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<p>The accuracy of predicting ice phase changes on transmission lines is influenced by the interaction of various meteorological factors. However, existing models, which ignore the interactions between multiple physical fields, result in insufficient prediction accuracy and other issues. To address this, a multi-physical field coupling ice phase change prediction model has been developed. First, by setting up multi-physical field coupling boundary conditions, the model simulates various meteorological conditions to create a three-dimensional geometric model of ice phase changes on transmission lines. Then, different kernel functions are selected to verify the reliability of the support vector machine (SVM) prediction algorithm. Finally, the multi-physical field coupling model is used for ice phase change prediction experiments, and its effectiveness is verified through comparison with actual measurement data and SVM prediction data. The experimental results show that the model has an average prediction accuracy of 98.12% and an average precision of 98.54%, significantly improving upon traditional methods. This model provides high-precision decision support for the early warning and protection of ice disasters on transmission lines, making it highly valuable for engineering applications.</p>

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Study on ice phase change prediction model for transmission lines based on multiphysics coupling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Yingbo Pei,
  • Qingbin Wang,
  • Liang Wang,
  • Shuai Zuo,
  • Hui Zhang,
  • Lantao Jing

摘要

The accuracy of predicting ice phase changes on transmission lines is influenced by the interaction of various meteorological factors. However, existing models, which ignore the interactions between multiple physical fields, result in insufficient prediction accuracy and other issues. To address this, a multi-physical field coupling ice phase change prediction model has been developed. First, by setting up multi-physical field coupling boundary conditions, the model simulates various meteorological conditions to create a three-dimensional geometric model of ice phase changes on transmission lines. Then, different kernel functions are selected to verify the reliability of the support vector machine (SVM) prediction algorithm. Finally, the multi-physical field coupling model is used for ice phase change prediction experiments, and its effectiveness is verified through comparison with actual measurement data and SVM prediction data. The experimental results show that the model has an average prediction accuracy of 98.12% and an average precision of 98.54%, significantly improving upon traditional methods. This model provides high-precision decision support for the early warning and protection of ice disasters on transmission lines, making it highly valuable for engineering applications.